Earticle

초록

영어

This paper introduces a decision-making framework for offloading tasks in home network environments, utilizing Distributed Reinforcement Learning (DRL). The proposed scheme optimizes energy efficiency while maintaining system reliability within a lightweight edge computing setup. Effective resource management has become crucial with the increasing prevalence of intelligent devices. Conventional methods, including on-device processing and offloading to edge or cloud systems, need help to balance energy conservation, response time, and dependability. To tackle these issues, we propose a DRL-based scheme that allows flexible and enhanced decision-making regarding offloading. Simulation results demonstrate that the proposed method outperforms the baseline approaches in reducing energy consumption and latency while maintaining a higher success rate. These findings highlight the potential of the proposed scheme for efficient resource management in home networks and broader IoT environments.

목차

Abstract
1. Introduction
2. Related Work
2.1 Edge Computing and Offloading Techniques
2.2 Offloading Decisions Using Reinforcement Learning
2.3 Lightweight Edge Computing in Home Networks
3. System Model
3.1 Energy Consumption and Delay
3.2 Problem Statement
4. Proposed Scheme
4.1 DRL-based Offloading Decision
4.2 Training Process of the Proposed Scheme
5. Performance Evaluation
6. Conclusion
Acknowledgment
References

키워드

저자

• Ducsun Lim [ Post-Doc, Department of Computer Software, Hanyang University, Korea ]
• Dongkyun Lim [ Professor, Department of Computer Science Engineering, Hanyang Cyber University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380